A Technical Review of the State-of-the-Art Methods in Aspect-Based Sentiment Analysis

Authors

  • Kabir Kasum Yusuf Federal University Lokoja, Kogi State, Nigeria https://orcid.org/0009-0002-4890-2655
  • Emeka Ogbuju Federal University Lokoja, Kogi State, Nigeria
  • Taiwo Abiodun Federal University Lokoja
  • Francisca Oladipo Thomas Adewunmi University

DOI:

https://doi.org/10.62411/jcta.9999

Keywords:

Aspect-based sentiment analysis, Big data, Sentiment analysis, Systematic review, Text mining

Abstract

With the advent and rapid advancement of text mining technology, a computer-based approach used to capture sentiment standpoints from data in textual form is increasingly becoming a promising field. Detailed information about sentiment can be provided using aspect-based sentiment analysis, which can be used in better decision-making. This study aims to study, observe, and classify previous methods used in aspect-based sentiment analysis. A systematic review is adopted as the method used to collect and review papers to achieve this research's aim. Papers focused on sentiment analysis, aspect extraction, and aspect aggregation from different academic databases such as Scopus, ScienceDirect, IEEE Explore, and Web of Science were gathered based on the inclusion and exclusion criteria of the study. The gathered papers were further reviewed to answer the stated research questions. The findings from the research show the most used methods for aspect extraction, sentiment analysis, and aspect aggregation in aspect-based sentiment analysis. This research offers a robust synthesis of evidence to guide further academic exploration in sentiment analysis.

Author Biography

Kabir Kasum Yusuf, Federal University Lokoja, Kogi State, Nigeria

MSC STUDENT; DEPARTMENT OF COMPUTER SCIENCE, FEDERAL UNIVERISTY LOKOJA, KOGI STATE, NIGER

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Published

2024-02-21

How to Cite

Yusuf, K. K., Ogbuju, E., Abiodun, T., & Oladipo, F. (2024). A Technical Review of the State-of-the-Art Methods in Aspect-Based Sentiment Analysis. Journal of Computing Theories and Applications, 1(3), 287–298. https://doi.org/10.62411/jcta.9999